code: def __init__(
      self,
      ctx: Context,
      in_features_a,
      in_features_b,
      out_features,
      rank_a,
      rank_b,
      wa_init_fn: typing.Callable[[typing.Tuple], torch.Tensor],
      wb_init_fn: typing.Callable[[typing.Tuple], torch.Tensor],
      precision_bits=None,
  ):
      self.group = ctx.mpc.communicator.new_group(
          [rank_a, rank_b],
          f"{ctx.namespace.federation_tag}.sshe_nn_aggregator_layer",
      )
      self.aggregator = SSHENeuralNetworkAggregator(
          ctx,
          in_features_a=in_features_a,
          in_features_b=in_features_b,
          out_features=out_features,
          rank_a=rank_a,
          rank_b=rank_b,
          group=self.group,
          encoder=FixedPointEncoder(precision_bits),
          wa_init_fn=wa_init_fn,
          wb_init_fn=wb_init_fn,
      )
      super().__init__()
  
imports: import typing
  from typing import Any, Iterator
  import torch
  from torch.nn import Parameter
  from fate.arch.context import Context
  from fate.arch.protocol.mpc.common.encoding import IgnoreEncodings
  from fate.arch.protocol.mpc.mpc import FixedPointEncoder
  from fate.arch.trace import auto_trace
label_desc: Use image node_id 1 to create a new SSHENeuralNetworkAggregatorLayer object from inherited base classes: torch.nn with example: obj = SSHENeuralNetworkAggregatorLayer(ctx, in_features_a, in_features_b, out_features, rank_a, rank_b, wa_init_fn, wb_init_fn, precision_bits)